Paste delimited data
Use the first row for unique column names and add one record per row.
Convert CSV, semicolon, tab, or pipe-delimited text into a JSON array while preserving every cell as text and handling inconsistent rows explicitly.
Three simple steps, with your content kept on your device.
Use the first row for unique column names and add one record per row.
Select the delimiter and decide whether inconsistent rows stop or skip.
Review the JSON object array and save the local result.
Fast, focused, and made to be clear on every screen.
Handles delimiters, line breaks, and escaped double quotes inside quoted fields.
Keeps every cell as a string so leading zeros and large numeric-looking values are not changed.
Reports inconsistent rows or skips them only when you choose that policy.
CSV to JSON conversion turns a comma-separated values file into a JSON array of objects or arrays. Each row becomes one element, with the header row naming the keys when it is present.
The result is structured data that fits naturally into web applications, APIs, and JavaScript code, where JSON is the native format.
CSV files use delimiters — commonly a comma — to separate columns, with quotes around values that contain the delimiter itself. The converter reads this structure and preserves quoted fields correctly.
Header rows, when present, become the keys of each object. Files without headers convert to arrays of arrays, keeping the data intact either way.
CSV is compact and works everywhere, but it is flat: rows and columns only. JSON is nested and typed, which lets it represent objects, arrays, and hierarchies that CSV cannot express.
Converting CSV to JSON is the standard step when data must move from a spreadsheet or export into a programmatic structure.
Paste the CSV or load the file, choose whether the first row is a header, and read the generated JSON. The output is ready to copy into code, an API payload, or a database import.
The conversion runs locally, so large datasets are handled on your device without any upload.
Real CSV files often contain rows with the wrong number of columns. The converter reports these so you can see exactly where the data diverges from the expected structure.
You can choose to skip inconsistent rows or keep the strict output, depending on how the data will be consumed.
The two converters are reverse operations: CSV to JSON structures flat tabular data, while JSON to CSV flattens structured data back into rows. They share the same data but solve opposite problems.
Valestiom provides both, so you can move data in either direction without switching tools.
The conversion runs entirely in your browser. Your CSV and the resulting JSON never leave your device, so you can convert sensitive datasets without uploading them anywhere.
That local-first behavior is the same privacy model used across Valestiom tools.
Spreadsheets and database exports commonly produce CSV, which makes the format the bridge between tabular tools and code. Converting that CSV to JSON is the natural next step for web development.
Export the sheet, convert it here, and paste the JSON directly into your application or API.
A typical pipeline moves data from a spreadsheet into JSON, and later back out to CSV for sharing. With both converters on Valestiom, the round trip happens in one place.
The conversions run locally at every step, so the data stays on your device throughout.
After conversion, scan the JSON for expected keys and values before using it. The header names become keys, so a quick check confirms the mapping matches what your code expects.
Because every cell stays a string, values like identifiers and dates are preserved exactly — a deliberate choice that avoids silent type changes.
Clean source data converts best: consistent columns, a single header row, and values that do not contain stray delimiters. Most exported spreadsheets already satisfy these conditions.
When a file contains notes or extra blank rows, trimming them before conversion keeps the JSON output predictable.
No. Every CSV cell remains a JSON string, which avoids changing identifiers, leading zeros, dates, or large numbers.
Wrap the field in double quotes and represent a literal double quote with two double quotes.
Malformed quoting is rejected. Rows with the wrong field count either stop conversion or are skipped according to your selection.